{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xu Y"],"funding":["Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada","CSU | CSU Bakersfield | School of Natural Sciences, Mathematics, and Engineering, California State University, Bakersfield","Canada Research Chairs"],"pagination":["13686"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6757104"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_abstract":["Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The traditional SNP-wise approach along with multiple testing adjustment is over-conservative and lack of power in many GWASs. In this article, we proposed a model-based clustering method that transforms the challenging high-dimension-small-sample-size problem to low-dimension-large-sample-size problem and borrows information across SNPs by grouping SNPs into three clusters. We pre-specify the patterns of clusters by minor allele frequencies of SNPs between cases and controls, and enforce the patterns with prior distributions. In the simulation studies our proposed novel model outperforms traditional SNP-wise approac"],"journal":["Scientific reports"],"pubmed_title":["Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies."],"pmcid":["PMC6757104"],"funding_grant_id":["RGPIN-2017-04722","Postdoc Fellowship","950-231363"],"pubmed_authors":["Qiu W","Xing L","Zhang X","Su J","Xu Y"],"additional_accession":[]},"is_claimable":false,"name":"Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies.","description":"Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The traditional SNP-wise approach along with multiple testing adjustment is over-conservative and lack of power in many GWASs. In this article, we proposed a model-based clustering method that transforms the challenging high-dimension-small-sample-size problem to low-dimension-large-sample-size problem and borrows information across SNPs by grouping SNPs into three clusters. We pre-specify the patterns of clusters by minor allele frequencies of SNPs between cases and controls, and enforce the patterns with prior distributions. In the simulation studies our proposed novel model outperforms traditional SNP-wise approac","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Sep","modification":"2026-05-03T19:33:28.876Z","creation":"2019-10-11T07:10:11Z"},"accession":"S-EPMC6757104","cross_references":{"pubmed":["31548641"],"doi":["10.1038/s41598-019-50229-6"]}}